However, this type of measurement blends together the churn rates of new customers and existing customers.

Shiny new customers that signed-up that month are lumped together with long-standing customers, despite the fact that:

Churn rates are likely to vary between the different groups.

New customers and existing customers will have very different reasons for churning.

Invisible Problems

This blending together of churn rates creates invisible problems for your business.

For example, you may have a fantastic onboarding process, but lousy customer support.

Happy new customers will contribute to low churn in the first few months, but mounting frustrations further down the line will lead to high churn later in life.

Unfortunately, if we used a simple average measurement of churn each month, the low churn rates of new customers would offset the high churn rates of existing customers.

We wouldn't gain any insight into the success of our onboarding process, or the failure of our customer support. Without that information, we couldn't fix the problem.

The Solution: Cohort Analysis

That's where cohort analysis comes in.

Instead of grouping all of our customers together each month, we can seperate them out into more meaningful groups (known as cohorts).

Each customer within a cohort will share a similar experience or characteristic.

In the example below, customers are grouped horizontally according to the month they became paying customers. Vertically, they're seperated out according to the length of time they've been customers. And along the bottom you've got the average percentage of retained customers for that lifetime month, which will help you identify trends in your data.